def save_html(confusion_matrix_folder, ground_truths, predictions, model):
html_folder = join(confusion_matrix_folder, 'html')
makedirs(html_folder, exist_ok=True)
cm = ConfusionMatrix(actual_vector=ground_truths,
predict_vector=predictions)
filename = f'{model}.html'
cm.save_html(join(html_folder, filename), summary=True)
def ClassificationReport(ytrue, ypred, plotcm=False, file=None, **options):
from sklearn.metrics import classification_report, cohen_kappa_score, matthews_corrcoef, confusion_matrix
a = options.get('adj_left', 0.1)
b = options.get('adj_bottom', 0.2)
figsize = options.get('figsize', [4, 4])
axes_size = options.get('axes_size', 22)
# noinspection PyUnresolvedReferences
cmap = options.get('cmap', plt.cm.RdPu)
fontsize = options.get('fontsize', 26)
xrot = options.get('xrot', 0)
show = options.get('show', True)
acc = cohen_kappa_score(ytrue, ypred)
cm = confusion_matrix(ytrue, ypred)
matt = matthews_corrcoef(ytrue, ypred)
cr = classification_report(ytrue, ypred)
print(f'\nConfusion matrix:\n{cm}')
print(f'\nCohen kappa score : {np.round(acc, 3)}')
print(f'Matthews correlation coef: {np.round(matt, 3)}\n')
print(cr)
if file is not None:
from pycm import ConfusionMatrix
path_ = os.path.dirname(file)
if not os.path.exists(path_):
os.makedirs(path_)
CM = ConfusionMatrix(ytrue, ypred)
CM.save_html(file)
if plotcm:
fig, _ = customplot(adj_bottom=b,
adj_left=a,
figsize=figsize,
axes_size=axes_size)
plot_confusion_matrix(cm,
classes=np.unique(ytrue),
cmap=cmap,
fontsize=fontsize,
xrot=xrot)
if show:
plt.show()
return fig
class Train:
"""
==========================================================
Training procedure for classification and regression model
==========================================================
Train(random_state, cv, mode, colors, scaler, multiclassroc, verbose)
Methods:
- fit(x, y)
- evaluate(x, y)
- predict(x)
- classification_report(external=bool, intercv=bool)
- scores_()
- estimator_()
- plotcm(external, intercv, print_stats, adj_left, adj_bottom, fromatplot, title)
- save_stats(file)
- pointplot_(adj_left, adj_bottom, cross_val_predict)
- results()
- plotregression_(adj_left, adj_bottom_cross_val_predict)
"""
def __init__(self, estimator, **options):
from sklearn.model_selection import StratifiedKFold
self.X_train = None
self.X_test = None
self.y_train = None
self.y_test = None
self.intercv = None
self.estimator = estimator
self.random_state = options.get('random_state', 99)
self.cv = options.get(
'cv',
StratifiedKFold(n_splits=10,
shuffle=True,
random_state=self.random_state))
self.mode = options.get('mode', 'classification')
self.colors = options.get('colors', None)
self.scaler = options.get('scaler', StandardScaler())
self.multiroc = options.get('multiclassroc', True)
self.verbose = options.get('verbose', 2)
self.compact = options.get('compact', True)
self.internal = list()
self.external = list()
self.training = list()
self.crossValPredict = list()
self.clf = list()
self.CM = list()
self.predictCVDF = pd.DataFrame()
self.predictTrainDF = pd.DataFrame()
self.predictTestDF = pd.DataFrame()
def __repr__(self):
if self.compact:
return f'Scaling : {self.scaler!r}\nEstimator: {self.estimator!r}\n'
else:
return f'Estimator: {self.estimator!r}\n'
def fit(self, *arrays):
from sklearn.pipeline import Pipeline
from nanolib.utils import plotroc
if len(arrays) == 2:
self.X_train = arrays[0]
self.y_train = arrays[1]
self.intercv = True
else:
self.X_train = arrays[0]
self.X_test = arrays[1]
self.y_train = arrays[2]
self.y_test = arrays[3]
self.intercv = False
# classification using accuracy metric
if self.mode == 'classification':
if self.compact:
pipe = Pipeline([('scaler', self.scaler),
('clf', self.estimator)])
else:
pipe = self.estimator
self.internal = cross_val_score(pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring=None)
self.crossValPredict = cross_val_predict(pipe,
self.X_train,
self.y_train,
cv=self.cv,
n_jobs=-1)
self.clf = pipe.fit(self.X_train, self.y_train)
self.training = self.clf.predict(self.X_train)
predTrain = self.clf.predict(self.X_train)
self.predictTrainDF = pd.concat([
pd.DataFrame(data=self.y_train, columns=['Actual']),
pd.DataFrame(data=predTrain, columns=['Prediction']),
],
axis=1)
self.predictCVDF = pd.concat([
pd.DataFrame(data=self.y_train, columns=['Actual']),
pd.DataFrame(data=self.crossValPredict, columns=['Prediction'])
],
axis=1)
print('Metric - accuracy_score:')
internalcv = cross_val_score(pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring=None)
print(
f'Mean of Internal-Validation : {round(np.mean(internalcv), 3)}'
)
print(
f'Stdev of Internal-Validation : {round(np.std(internalcv), 3)}'
)
print(
f'Training score : {round(accuracy_score(self.y_train, self.training), 3)}\n'
)
print('Metric - F1-Score (macro):')
scoring = make_scorer(f1_score, average='macro')
internalcv = cross_val_score(pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring=scoring)
print(
f'Mean of Internal-Validation : {round(np.mean(internalcv), 3)}'
)
print(
f'Stdev of Internal-Validation : {round(np.std(internalcv), 3)}'
)
print(
f'Training score : {round(f1_score(self.y_train, self.training, average="macro"), 3)}\n'
)
if not self.intercv:
self.external = self.clf.predict(self.X_test)
self.predictTestDF = pd.concat([
pd.DataFrame(data=self.y_test, columns=['Actual']),
pd.DataFrame(data=self.external, columns=['Prediction'])
],
axis=1)
print(
f'External-validation accuracy score : {round(accuracy_score(self.y_test, self.external), 3)}\n'
)
print(
f'External-validation F1-score (macro): {round(f1_score(self.y_test, self.external, average="macro"), 3)}\n'
)
# regression using rsquared metric
elif self.mode == 'regression':
if self.compact:
pipe = Pipeline([('scaler', self.scaler),
('clf', self.estimator)])
else:
pipe = self.estimator
self.crossValPredict = cross_val_predict(pipe,
self.X_train,
self.y_train,
cv=self.cv,
n_jobs=-1)
self.predictCVDF = pd.concat([
pd.DataFrame(data=self.y_train, columns=['Actual']),
pd.DataFrame(data=self.crossValPredict, columns=['Prediction'])
],
axis=1)
self.internal = cross_val_score(pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring='r2')
print('Internal-Validation Score')
print(f'Mean of R2 score : {np.mean(self.internal)}')
print(f'Stdev of R2 score : {np.std(self.internal)}')
print(
f'Mean of adj-R2 score : {np.mean(self.adj_r2_squared(self.internal, self.X_train, self.y_train))}'
)
print(
f'Stdev of adj-R2 score : {np.std(self.adj_r2_squared(self.internal, self.X_train, self.y_train))}'
)
self.internal = cross_val_score(
pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring='neg_root_mean_squared_error')
print(f'Mean of RMSE score : {np.mean(self.internal)}')
print(f'Stdev of RMSE score : {np.std(self.internal)}\n')
self.internal = cross_val_score(pipe,
self.X_train,
self.y_train,
n_jobs=-1,
cv=self.cv,
scoring='neg_mean_absolute_error')
print(f'Mean of MAE score : {np.mean(self.internal)}')
print(f'Stdev of MAE score : {np.std(self.internal)}\n')
self.clf = pipe.fit(self.X_train, self.y_train)
self.training = self.clf.predict(self.X_train)
pred_training = pd.DataFrame(data=self.training,
columns=['Prediction'])
self.predictTrainDF = pd.concat([
pd.DataFrame(data=self.y_train, columns=['Actual']),
pred_training
],
axis=1)
print('Training Score ')
print(
f'R2 score : {r2_score(self.y_train, self.training)}'
)
print(
f'adj-R2 score : {self.adj_r2_squared(r2_score(self.y_train, self.training), self.X_train, self.y_train)}'
)
print(
f'RMSE score : {mean_squared_error(self.y_train, self.training)}'
)
print(
f'MAE score : {mean_absolute_error(self.y_train, self.training)}\n'
)
if not self.intercv:
self.external = self.clf.predict(self.X_test)
pred_external = pd.DataFrame(data=self.external,
columns=['Prediction'])
self.predictTestDF = pd.concat([
pd.DataFrame(data=self.y_test, columns=['Actual']),
pred_external
],
axis=1)
print('External-Validation Score')
print(
f'R2 score : {r2_score(self.y_test, self.external)}'
)
print(
f'adj-R2 score : {self.adj_r2_squared(r2_score(self.y_test, self.external), self.X_test, self.y_test)}'
)
print(
f'RMSE score : {mean_squared_error(self.y_test, self.external)}'
)
print(
f'MAE score : {mean_absolute_error(self.y_test, self.external)}\n'
)
elif self.mode == 'roc':
from sklearn.multiclass import OneVsRestClassifier
clf = OneVsRestClassifier(self.estimator)
if self.compact:
pipe = Pipeline([('scaler', self.scaler), ('clf', clf)])
else:
pipe = clf
y_score = clf.fit(self.X_train,
self.y_train).decision_function(self.X_test)
fig, res = plotroc(self.y_test,
y_score,
colors=self.colors,
multiclass=self.multiroc)
return fig, res
else:
return print(
f'Your input scoring is {self.scoring} that not fit with one of accuracy, rsquared, and roc'
)
def predict(self, *array):
return self.clf.predict(array[0])
def evaluate(self, *arrays):
self.X_test = arrays[0]
self.y_test = arrays[1]
self.external = self.clf.predict(self.X_test)
if self.mode == 'classification':
self.predictTestDF = pd.concat([
pd.DataFrame(data=self.y_test, columns=['Actual']),
pd.DataFrame(data=self.external, columns=['Prediction'])
],
axis=1)
print(
f'External-validation accuracy score : {accuracy_score(self.y_test, self.external)}\n'
)
# print(f'External-validation F1-score (micro): {f1_score(self.y_test, self.external, average="micro")}\n')
print(
f'External-validation F1-score (macro): {f1_score(self.y_test, self.external, average="macro")}\n'
)
elif self.mode == 'regression':
pred_external = pd.DataFrame(data=self.external,
columns=['Prediction'])
self.predictTestDF = pd.concat([
pd.DataFrame(data=self.y_test, columns=['Actual']),
pred_external
],
axis=1)
print('External-Validation Score')
print(
f'R2 score : {r2_score(self.y_test, self.external)}'
)
print(
f'adj-R2 score : {self.adj_r2_squared(r2_score(self.y_test, self.external), self.X_test, self.y_test)}'
)
print(
f'RMSE score : {mean_squared_error(self.y_test, self.external)}'
)
print(
f'MAE score : {mean_absolute_error(self.y_test, self.external)}\n'
)
def scores_(self):
if self.intercv:
return self.internal, self.training, self.crossValPredict
else:
return self.internal, self.training, self.crossValPredict, self.external
def estimator_(self):
return self.clf
def classification_report(self, external=False, intercv=False, file=None):
from sklearn.metrics import classification_report, cohen_kappa_score, matthews_corrcoef
if self.mode == 'classification':
if external:
y_true = self.y_test
y_pred = self.clf.predict(self.X_test)
info = 'external-validation'
else:
y_true = self.y_train
if intercv:
info = 'internal-validation'
y_pred = self.crossValPredict
else:
info = 'training-validation'
y_pred = self.clf.predict(self.X_train)
acc = cohen_kappa_score(y_true, y_pred)
cm = confusion_matrix(y_true, y_pred)
matt = matthews_corrcoef(y_true, y_pred)
cr = classification_report(y_true, y_pred, digits=3)
print(f'\nConfusion matrix for {info}:\n{cm}')
print(f'\nCohen kappa score for {info}: {np.round(acc, 3)}')
print(
f'Matthews correlation coef for {info}: {np.round(matt, 3)}\n')
print(cr)
if file != None:
self.CM = ConfusionMatrix(actual_vector=y_true,
predict_vector=y_pred)
self.CM.save_html(f'{str(file)}-{info}')
def plotcm(self, external=False, intercv=False, **options):
from nanolib.utils import plot_confusion_matrix
a = options.get('adj_left', 0.1)
b = options.get('adj_bottom', 0.2)
ps = options.get('print_stats', False)
title = options.get('title', False)
figsize = options.get('figsize', [5, 5])
axes_size = options.get('axes_size', 22)
if self.mode == 'classification':
if external:
y_true = self.y_test
y_pred = self.clf.predict(self.X_test)
else:
y_true = self.y_train
if intercv:
y_pred = self.crossValPredict
else:
y_pred = self.clf.predict(self.X_train)
cm_ = confusion_matrix(y_true, y_pred)
np.set_printoptions(precision=2)
class_names = np.unique(y_true)
self.CM = ConfusionMatrix(actual_vector=y_true,
predict_vector=y_pred)
if ps:
print(self.CM)
fig, _ = customplot(adj_bottom=b,
adj_left=a,
figsize=figsize,
axes_size=axes_size)
if title:
if external:
plt.title('External-Validation')
else:
if intercv:
plt.title('Internal-Validation')
else:
plt.title('Training Results')
plot_confusion_matrix(cm_, classes=class_names)
plt.show()
return fig
else:
print('Just for classification')
# noinspection PyUnresolvedReferences
def save_stats(self, **kwargs):
fullpathname = kwargs.get('file', 'cm')
self.CM.save_html(fullpathname)
def pointplot_(self, **options):
import math
yint = range(min(self.y_train), math.ceil(max(self.y_train)) + 1)
a = options.get('adj_left', 0.12)
b = options.get('adj_bottom', 0.12)
mode = options.get('cross_val_predict', False)
if mode:
trainDF = self.predictCVDF
else:
trainDF = self.predictTrainDF
trainDF = trainDF.sort_values(by='Actual')
if self.intercv:
fig, ax = customplot(adj_left=a, adj_bottom=b)
plt.plot(trainDF.Actual.values, '--r')
plt.plot(trainDF.Prediction.values, '-bo')
plt.xlabel('Samples')
plt.ylabel('Class prediction')
plt.yticks(yint)
return fig
else:
testDF = self.predictTestDF
testDF = testDF.sort_values(by='Actual')
fig, ax = customplot(adj_bottom=b, adj_left=a)
plt.subplot(2, 1, 1)
plt.plot(trainDF.Actual.values, '--r')
plt.plot(trainDF.Prediction.values, '-bo')
plt.ylabel('Train prediction')
plt.yticks(yint)
plt.subplot(2, 1, 2)
plt.plot(testDF.Actual.values, '--r')
plt.plot(testDF.Prediction.values, '-bo')
plt.ylabel('Test prediction')
plt.xlabel('Samples')
plt.yticks(yint)
return fig
def results(self):
if self.intercv:
return self.predictTrainDF, self.predictCVDF
else:
return self.predictTrainDF, self.predictCVDF, self.predictTestDF
@staticmethod
def abline(slope, intercept):
axes = plt.gca()
x_vals = np.array(axes.get_xlim())
y_vals = intercept + slope * x_vals
plt.plot(x_vals, y_vals, '--')
@staticmethod
def adj_r2_squared(r2, X, y):
return 1 - (1 - r2) * (len(y) - 1) / (len(y) - X.shape[1] - 1)
def plotregression_(self, **options):
a = options.get('adj_left', 0.12)
b = options.get('adj_bottom', 0.12)
mode = options.get('cross_val_predict', False)
fig, ax = customplot(adj_left=a, adj_bottom=b)
if mode:
ax.scatter(self.predictCVDF.Actual,
self.predictCVDF.Prediction,
s=70,
c='b',
marker='o',
label='Training')
else:
ax.scatter(self.predictTrainDF.Actual,
self.predictTrainDF.Prediction,
s=70,
c='b',
marker='o',
label='Training')
if not self.intercv:
ax.scatter(self.predictTestDF.Actual,
self.predictTestDF.Prediction,
s=70,
c='r',
marker='v',
label='Testing')
ax.legend()
self.abline(1, 0)
plt.xlabel('Actual')
plt.ylabel('Prediction')
return fig
def _analyze_stats(results_dir, sf_lf_map, correct_str, errors_str, sf_confusion, id_map, experiment=None):
"""
:param results_dir: directory in which to write results
:param sf_lf_map: Map of SFs to candidate LFs (i.e. AA --> {amino acids, alcoholics anonymous, etc.})
:param correct_str: String containing prettified versions of all correctly predicted examples
:param errors_str: String containing prettified versions of all incorrectly predicted examples
:param sf_confusion: dictionary of confusion matrices for SFs
:param id_map: Dictionary with keys 'correct' and 'error'. Values are example ids. For generating inter-model
confusion matries
:param experiment: name of experiment in which to write results in side of results_dir/
:return:
"""
correct_fp = os.path.join(results_dir, 'correct.txt')
reports_fp = os.path.join(results_dir, 'reports.txt')
errors_fp = os.path.join(results_dir, 'errors.txt')
summary_fp = os.path.join(results_dir, 'summary.csv')
id_fp = os.path.join(results_dir, 'error_tracker.json')
df = defaultdict(list)
rare_lfs = get_rare_lfs()
rare_recalls = []
cols = [
'sf',
'experiment',
'support',
'num_targets',
'micro_precision',
'micro_recall',
'micro_f1',
'macro_precision',
'macro_recall',
'macro_f1',
'weighted_precision',
'weighted_recall',
'weighted_f1',
]
with open(id_fp, 'w') as fd:
json.dump(id_map, fd)
reports = []
with open(correct_fp, 'w') as fd:
for k in sorted(correct_str.keys()):
fd.write(correct_str[k])
with open(errors_fp, 'w') as fd:
for k in sorted(errors_str.keys()):
fd.write(errors_str[k])
for sf in sf_confusion:
labels = sf_lf_map[sf]
labels_trunc = list(map(lambda x: x.split(';')[0], labels))
y_true = sf_confusion[sf][0]
y_pred = sf_confusion[sf][1]
sf_results = classification_report(y_true, y_pred, labels=list(range(len(labels_trunc))),
target_names=labels_trunc, output_dict=True)
report = classification_report(y_true, y_pred, labels=list(range(len(labels_trunc))),
target_names=labels_trunc)
macro_nonzero = defaultdict(float)
num_nonzero = 0
for orig_lf, lf in zip(labels, labels_trunc):
if orig_lf in rare_lfs:
rare_recalls.append(sf_results[lf]['recall'])
d = sf_results[lf]
if d['support'] > 0:
macro_nonzero['precision'] += d['precision']
macro_nonzero['recall'] += d['recall']
macro_nonzero['f1-score'] += d['f1-score']
num_nonzero += 1
for suffix in ['precision', 'recall', 'f1-score']:
sf_results['macro avg'][suffix] = macro_nonzero[suffix] / float(num_nonzero)
reports.append(report)
reports.append('\n\n')
metrics = ['micro avg', 'macro avg', 'weighted avg']
for metric in metrics:
if metric in sf_results:
for k, v in sf_results[metric].items():
if not k == 'support':
metric_key = '{}_{}'.format(metric.split(' ')[0], k.split('-')[0])
df[metric_key].append(v)
else:
for suffix in ['precision', 'recall', 'f1']:
df['{}_{}'.format(metric.split(' ')[0], suffix)].append(None)
df['sf'].append(sf)
df['num_targets'].append(len(labels_trunc))
df['support'].append(sf_results['weighted avg']['support'])
try:
cm = ConfusionMatrix(actual_vector=y_true, predict_vector=y_pred)
label_idx_to_str = dict()
for idx in cm.classes:
label_idx_to_str[idx] = labels_trunc[int(idx)]
cm.relabel(mapping=label_idx_to_str)
cm_outpath = os.path.join(results_dir, 'confusion', sf)
cm.save_html(cm_outpath)
except:
print('Only 1 target class for test set SF={}'.format(sf))
df['experiment'] = [experiment] * len(df['sf'])
summary_df = pd.DataFrame(df, columns=cols)
summary_df.to_csv(summary_fp, index=False)
with open(reports_fp, 'w') as fd:
for report in reports:
fd.write(report)
suffixes = ['precision', 'recall', 'f1']
types = ['weighted', 'macro']
agg_metrics = {}
for t in types:
for suffix in suffixes:
key = '{}_{}'.format(t, suffix)
avg_val = summary_df[key].mean()
print('Global {} --> {}'.format(key, avg_val))
agg_metrics[key] = avg_val
num_targets = summary_df['num_targets'].unique().tolist()
print('Num Targets, Macro F1, Weighted F1')
for t in sorted(num_targets):
avg_macro_f1 = summary_df[summary_df['num_targets'] == t]['macro_f1'].mean()
avg_weighted_f1 = summary_df[summary_df['num_targets'] == t]['weighted_f1'].mean()
print('{},{},{}'.format(t, avg_macro_f1, avg_weighted_f1))
rare_recall = sum(rare_recalls) / float(len(rare_recalls))
num_rare = len(rare_recalls)
print('Recall on {} rare long forms: {}'.format(num_rare, rare_recall))
return agg_metrics